PyData 2015 Keynote: "A Systems View of Machine Learning"
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Despite the growing abundance of powerful tools, building and deploying machine-learning frameworks into production continues to be major challenge, in both science and industry. I'll present some particular pain points and cautions for practitioners as well as recent work addressing some of the nagging issues. I advocate for a systems view, which, when expanded beyond the algorithms and codes to the organizational ecosystem, places some interesting constraints on the teams tasked with development and stewardship of ML products. About: Dr. Joshua Bloom is an astronomy professor at the University of California, Berkeley where he teaches high-energy astrophysics and Python for data scientists. He has published over 250 refereed articles largely on time-domain transients events and telescope/insight automation. His book on gamma-ray bursts, a technical introduction for physical scientists, was published recently by Princeton University Press. He is also co-founder and CTO of wise.io, a startup based in Berkeley. Josh has been awarded the Pierce Prize from the American Astronomical Society; he is also a former Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. He holds a PhD from Caltech and degrees from Harvard and Cambridge University.
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PyData 2015 Keynote: "A Systems View of Machine Learning"
- 2. About Me…
- 3. http://research.google.com/pubs/pub43146.html • Complex models erode abstraction boundaries • Data dependencies cost more than code dependencies • System-level Spaghetti • Changing External World “It may be surprising to the academic community to know that only a fraction of the code … is actually doing ‘machine learning’. A mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code.”
- 4. Algorithms Software Hardware Project Staff Consumers Organization + Society ML System Components Agenda - inside-out discussion of component parts & some interconnects - presentation of some facilitating new tools - impact on problem definetion teams
- 5. Linear/ Logistic Regression Naive Bayes Decision Trees SVMs Bagging Boosting Decision Forests Neural Nets Deep Learning Nearest Neighbors Gaussian/ Dirichlet Processes Splines Lasso XGBoost …. Some Algos/Models/Approaches Used in Practice LDA/LSI RNN Software Instantiations in the Python Ecosystem BOW word2vec
- 6. Nguyen'et'al,'CVPR'2015' All Models of Learning Have Flaws http://hunch.net/?p=224 “It’s common to forget the flaws of the model that you are most familiar…while the flaws of new models get exaggerated.” - John Langford (2007, Microsoft research) Concepts$≠$Statistics Convolutional# networks# can#be# fooled.
- 7. Nguyen'et'al,'CVPR'2015' The$impact$of$dataset$bias Training/testing#on#biased#datasets#gives#unrealistic#results. !E.g.#:#Torralba and#Efros,#Unbiased2look2at2dataset2bias,#CVPR#2011. Torralba/Efros11 via L. Bottou (ICML 2015) All Models of Learning Have Flaws http://hunch.net/?p=224 “It’s common to forget the flaws of the model that you are most familiar…while the flaws of new models get exaggerated.” - John Langford (2007, Microsoft research) Concepts$≠$Statistics Convolutional# networks# can#be# fooled. (Nguyen#et#al,#CVPR#2015) Magri*e,'ICML,'1929'
- 8. What are you optimizing for?Component What Algorithm/Model Learning rate, convexity, error bounds, scaling, … + Software/Hardware Accuracy, Memory usage, Disk usage, CPU needs, time to learn, time to predict + Project Staff time to implement, people/ resource costs, reliability, maintainability, experimentability + Consumers direct value, useability, explainability, actionability + Society indirect value - multi-axis optimizations in a given component - highly coupled optimization considerations between components - myoptic view can be costly further up the stack
- 9. Scalar proxies: - RMSE - RMSLE - [adjusted] R2 - ... R2=0.91 RMSE = 692.3 Pearson R=0.96 Optimization Metric: What’s the essence of what I care about?
- 10. Scalar proxies: - RMSE - RMSLE - [adjusted] R2 - ... R2=0.91 RMSE = 692.3 Pearson R=0.96 scatter outliers bias Optimization Metric: What’s the essence of what I care about?
- 11. which classifier is best? depends... Optimization Metric: What’s the essence of what I care about?
- 13. 11 “We evaluated some of the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.” Xavier'Amatriain'and'Jus0n'Basilico'(April'2012) On the Prize
- 14. WiseFactory automated feature extraction, learning, prediction, deployment WiseTransfer efficient manipulation of large objects WiseDataSet WiseML high-productivity data science in Python WiseAlgorithm WindTunnel detect drift in CPU, Mem, Accuracy, Statistics Quality Wrapping High-Level API Deployment & Monitoring C++ SDK Core ML Stack at Wise.io G. Blanco D. Eads J. Richards P. Baines H. Brink
- 15. Wise DataSet BaseVariableGroup BaseVariableGroup BaseVariableGroup InstanceGroup InstanceGroup InstanceGroup RowSparse RowMajor HeterogeneousCache AlgoRepo ColSparse MemMapped Variable Mapper Level Mapper • fast, highly memory-efficient • heterogeneous • distributed Goal: easily surface algorithms (written in C++ to be cache exploitative) to Python WiseDataSets
- 16. Language-agnostic C++ Base Classes Python-specific Derived Classes Output Input Iterator Processor Array Processor String Processor FrameBuilder SeriesBuilder StringBuilder Frame Processor R-specific Derived Classes • expose flexible interface from Python, to high- performance, Python-agnostic C++ code • pass arbitrary data between layers using “Protocol Master” (like Protobufs) • write C++ code generically for GraphLab, Spark, pandas, and Wise WiseTransfer
- 17. Datasets for Data Science Comparison • ] • - Slicing Induces Copy Immutable Columns Query Transfer Speed to Python C++ SDK Distributed Memory Efficiency Categorical Optimized Sparse & Dense Pandas DataFrame Sequences No Yes N/A No No Medium Medium Yes GraphLab SFrame Yes Yes Yes Low Yes Yes High No Yes Spark DataFrame Yes Yes Yes Very Low No Yes Low No Yes Dask Yes No Yes N/A No Yes Medium No No Blaze No No Yes N/A No Yes Medium No No Wise DataSet Copy- on-write No Yes Very High Yes Yes Very High High Yes See also: Rob Story, today
- 18. Enforcing (Weak) Contracts: Monitoring Deployments Build DS workflow on test set, like the offline testing accuracy deploy & start monitoring results online, accuracy is worse than expected ? 1. Bang head to find (subtle) overfitting in model 2. Retrain: with new data (mo’ data, better answers) 3. Concept Drift: if retraining doesn’t help, jigger the DS workflow 4. Maybe that’s ok: Prediction influenced outcome. Hold out some live. What to do: see also, Chris Harland’s talk yesterday; Mike Manapat, today
- 19. unit tests Regression Tests Integration Tests Of course you’re doing this… ETL Testing is my contract affected by the (changing) update? Model Deployment Testing @treycausey (yesterday) some tools: Engarde Hypothesis Feature Forge Software Tests Enforcing (Weak) Contracts: Monitoring Deployments 1. Need to know when things are too different than before 2. Then alert a real human 3. Use automated tools to try to isolate cause of change: data or code.
- 20. reproducibility • every deployment & drift test given unique hash • generate data files & script with hash • Perform sampling on known-good deployments • Monitor RAM, CPU, accuracy metrics over time • Probabalistic testing component of our continuous integration of ML, 10k++ tests Wise “WindTunnel”
- 21. “Weak Contracts” ie. Abstractions within components bleed through to other components cf. Sculley … 1. A'smart'programmer'makes'an' invenQve'use'of'a'trained'object' recognizer.' 2. The'object'recognizer'receives'data'that' does'not'resemble'the'tesQng'data'and' outputs'nonsense.' 3. The'code'of'the'smart'programmer'does' not'work.' Example (via Bottou)
- 22. Platonic Form Data, as we act like it is… Plutonic Form …as it is.
- 23. NLP {broken: 3, “blue screen”: 2, ...} computer vision {eyes: [{“location”: [21,13], “bounding”: [...]}]..} metadata Sparse Dense{num_pages: 12, channel: “email”...} Nested 3rd party {author_klout: 34.0, ...} Missing/Noisy timeseries [2014-12-01T12:03:12, 2014-12-01T12:05:12] Streaming Real Data != Benchmark Data
- 26. http://mltsp.io pip install mltsp ML tsp. Machine Learning Time-Series Platform R. AllenM. SilverF. Peréz JSB Domain scientists AstroSeismoNeuro Funding bodies S. van der Walt A. Creillin-Quick Comp/ Stat/Eng An open-source web platform for distributed time-series analysis → •Selection of sophisticated feature extraction algorithms •Distributed computation •Sandboxed execution of custom code
- 27. Flask CLI (under developement) REST /learn /upload UI Disco W1 W2 Wn Disco worker pool datastore DB Demo!
- 28. -- MLTSP Continuous Integration github.com/drone github.com/mltsp/mltsp Test Container with MLTSP Custom Feature Extractor Sandbox Worker Pull request triggers webhook Workers- Disco SSH Drone calls GitHub status API
- 29. http://bigmacc.info Results from MLTSP The Astrophysical Journal Supplement Series, 203:32 (27pp), 2012 December Published Work before MLTSP MVP: Reproduce main results of a scientific paper
- 30. Probabilistic Classfication of Variable Stars Shivvers,JSB,Richards MNRAS,2014 106 “DEB” candidates 12 new mass-radii 15 “RCB/DYP” candidates 8 new discoveries Triple # of Galactic DYPer Stars Miller, Richards, JSB,..ApJ 2012 5400 Spectroscopic Targets Miller, JSB, Richards,..ApJ 2015 Turn synoptic imaged into ~spectrographs
- 31. WISE SUPPORT FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE FROM SUBJECT DESCRIPTION Support Ticket DATE TIER 1 AUTOMATED RESPONSE CUSTOMER FROM COMPLEXITY TO CLARITY INTELLIGENT ROUTING RECOMMENDED RESPONSE AUTOMATED REPLY Wise Support >30% faster avg. response time more consistent answers faster scaling of support teams
- 32. Fault Tolerant ML augmentation vs. full automation Random forest prediction of body segment in Xbox Kinect gmail
- 35. https://www.reddit.com/r/funny/comments/3e7gy4/yes_netflix_because_my_6_year_old_will_enjoy_the/ “Yes Netflix, because my 6 year old will enjoy the animated fun of Sons of Anarchy”
- 36. [So]'What'should'be'the'machine'learning'engineering'process?”' “Machine'learning'disrupts'so_ware'engineering' - Leon Bottou (Facebook)
- 37. ỉπ vs. (or “Data Science is a Team Sport”) deep domain skill/knowledge/training deep methodological knowledge/skill deep domain or methodological skill/knowledge/training strong methodological or domain knowledge/skill Goal: empower teams of gamma’s to excel ML Systems: It Takes a Village
- 38. ‣ Novel testing can strengthen abstractions within components, and contracts between ‣ Machine Learning Systems require optimizations across components - so we’d better understand the true loss function ‣ (End user) fault tolerance is a must Parting Thoughts ‣ Build ML into Systems because to have to…
- 39. Area Man Bites off more than he can chew PyData 2014